In [33]:
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import IsolationForest
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import matplotlib.pyplot as plt

i = 1
#canmbiar el valor de csv a agarrar para el entrenamiento
while (i < 71):
    # Path to your CSV
    path = "D:/UsX/Escritorio/space_apps_2024_seismic_detection/data/lunar/training/data/S12_GradeA/" + str(i) + ".csv"
    df = pd.read_csv(path)
    df.rename(columns={'time_abs(%Y-%m-%dT%H:%M:%S.%f)': 'date', 'time_rel(sec)': 'time', 'velocity(m/s)': 'velocity'}, inplace=True)

    # Interpolate missing data
    df.interpolate(method='linear', inplace=True)

    # --- Handle outliers (clip velocity values) ---
    # Clip values that are outside a reasonable range (modify thresholds based on your data)
    df['velocity'] = np.clip(df['velocity'], df['velocity'].quantile(0.01), df['velocity'].quantile(0.99))

    # Normalize the data
    scaler = MinMaxScaler()
    df[['velocity']] = scaler.fit_transform(df[['velocity']])

    # --- Smoothing the signal before feature extraction ---
    df['smoothed_velocity'] = df['velocity'].rolling(window=50, min_periods=1).mean()

    def extract_features(df, window_size=100):
        windows = []
        for start in range(0, len(df), window_size):
            window = df['smoothed_velocity'][start:start+window_size]
            if len(window) == window_size:
                windows.append([
                    np.mean(window),
                    np.std(window),
                    np.max(window),
                    np.min(window),
                    np.median(window)
                ])
        return np.array(windows)

    # Extract features using smoothed velocity
    features = extract_features(df)

    # --- Isolation Forest for anomaly detection ---
    iso_forest = IsolationForest(contamination=0.01, random_state=42)
    iso_forest.fit(features)

    # Predict anomalies (1 = normal, -1 = anomaly)
    anomalies_if = iso_forest.predict(features)

    # Identify anomaly indices
    anomaly_indices_if = np.where(anomalies_if == -1)[0]

    # --- Autoencoder for anomaly detection ---
    autoencoder = Sequential([
        Dense(32, activation='relu', input_shape=(features.shape[1],)),
        Dense(16, activation='relu'),
        Dense(32, activation='relu'),
        Dense(features.shape[1], activation='sigmoid')
    ])

    # Compile and train the autoencoder
    autoencoder.compile(optimizer='adam', loss='mse')
    autoencoder.fit(features, features, epochs=50, batch_size=32, shuffle=True)

    # Reconstruction error
    reconstructions = autoencoder.predict(features)
    mse = np.mean(np.power(features - reconstructions, 2), axis=1)

    # --- Adaptive thresholding based on reconstruction error ---
    threshold = np.percentile(mse, 95)  # Adjust threshold (95th percentile)
    anomalies_ae = mse > threshold
    anomaly_indices_ae = np.where(anomalies_ae)[0]

    # --- Visualization of anomalies ---
    plt.figure(figsize=(10, 6))
    plt.plot(df['time'], df['velocity'], label='Seismic Signal', alpha=0.2)
    
    # Mark anomalies from Isolation Forest in red
    plt.scatter(df['time'][anomaly_indices_if * 100], df['velocity'][anomaly_indices_if * 100], 
                color='red', label='Isolation Forest Anomalies', alpha=0.5)
    
    # Mark anomalies from Autoencoder in green
    plt.scatter(df['time'][anomaly_indices_ae * 100], df['velocity'][anomaly_indices_ae * 100], 
                color='blue', label='Autoencoder Anomalies', alpha=0.5)
    
    plt.xlabel('Time')
    plt.ylabel('Velocity')
    #title ='Anomalies Detected in Seismic Data: ', str(i)
    plt.title('Anomalies Detected in Seismic Data: ' + str(i))
    plt.legend()
    plt.show()

    # Plot the smoothed signal for identifying outliers or spikes
    plt.figure(figsize=(10, 6))
    plt.plot(df['time'], df['smoothed_velocity'])
    plt.xlabel('Time')
    plt.ylabel('Smoothed Velocity')
    plt.title('Smoothed Seismic Signal: ' + str(i))
    plt.show()

    i += 1
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0317
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7695e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3526e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8795e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9131e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.5831e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2092e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5075e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9379e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.8691e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.3733e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.0748e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1896e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0786e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0263e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0752e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.1374e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.2829e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.5435e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0832e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2445e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.8456e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.6553e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.2671e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.7939e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.9480e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.1042e-05 
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.5162e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 5.2110e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.2759e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3980e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.5900e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5367e-05 
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4566e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2979e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.7184e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6136e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5947e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8142e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8130e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2847e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5675e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.2559e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.0534e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7190e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.0880e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.7276e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5583e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1476e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.7267e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 938us/step - loss: 0.0174
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.6279e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.2754e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.5839e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1537e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6389e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0771e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0860e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8399e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.8185e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.5185e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3606e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4439e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6140e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2067e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0730e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0108e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.3080e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 9.6830e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 8.0137e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.8792e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.4042e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 5.1724e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.8520e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.9409e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 4.6317e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.8968e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.6117e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.3093e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 3.9186e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.5156e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0373e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 820us/step - loss: 3.3426e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 3.2873e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.5416e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.6700e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9856e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.8812e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.6373e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.4183e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5988e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.0467e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8169e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1376e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.1381e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5332e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3726e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7345e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5661e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7119e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0272
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.3948e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.4695e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.1194e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 891us/step - loss: 2.9978e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 851us/step - loss: 2.4836e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 958us/step - loss: 2.6180e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3169e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3848e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.9007e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.0303e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0490e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0610e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.1600e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 1.8641e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5907e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 8.3981e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.4679e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.1364e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.0362e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2187e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.0580e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0161e-04
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2838e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0064e-04
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.0741e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.3533e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.0778e-04
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.1393e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.7427e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 8.9872e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 4.1116e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 4.5490e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 6.0573e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 832us/step - loss: 4.8912e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.9024e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.9623e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2126e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 4.8459e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.2436e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3165e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.8685e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7865e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.2464e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8149e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0049e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1432e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6751e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7696e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.0299e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0264
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4843e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.9431e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0317e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1834e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0099e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1411e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6873e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0307e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7460e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1935e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7488e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1928e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0587e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.0263e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0673e-04  
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.2434e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3778e-04  
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.2799e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.5868e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3338e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3306e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.1809e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.2220e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 7.0043e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.7675e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.7270e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5168e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0879e-04
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.1205e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.3195e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.4110e-04
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0009e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.1669e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.1526e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.9875e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.1205e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 4.9559e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.3646e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9538e-05 
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.9978e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4204e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2657e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7548e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1256e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.6289e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 2.0125e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7500e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 2.1031e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5857e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 927us/step - loss: 0.0275
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2130e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8084e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2876e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9271e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9010e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.8398e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2235e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1291e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9110e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 7.8092e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3613e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4806e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1053e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.6999e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2907e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9213e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.6759e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4158e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.3902e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9797e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.5473e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 2.4408e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4827e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6625e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.0366e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.5654e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2902e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.7095e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2622e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1209e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7707e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1875e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2239e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8079e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9867e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.5413e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6343e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1514e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7260e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2617e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3480e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0831e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2515e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2295e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3169e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0897e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0260e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3712e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 8.6336e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0205
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.6618e-05
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.2800e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.0171e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.9733e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3898e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.4297e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7224e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0981e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.6492e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0735e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1071e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8020e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.9838e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2869e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.1878e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3818e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.1482e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.1682e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8281e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.2445e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9753e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3892e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8456e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3332e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.8801e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5911e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0936e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2956e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0697e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.4218e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3598e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2133e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4471e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0418e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3198e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0970e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9989e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.5584e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7576e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4873e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7409e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8613e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3388e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8120e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5906e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9060e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6633e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8412e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.3695e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 938us/step - loss: 0.0330
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3298e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.7407e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0560e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5234e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.1001e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.9027e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7406e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3307e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.0932e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.4932e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0070e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.2557e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.8593e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2724e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3718e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1726e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3616e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.0930e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.9823e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6288e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2832e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7220e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6712e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.4350e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.8470e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3836e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8516e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.2688e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.4431e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5491e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3393e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5877e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7482e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6930e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1366e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5192e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5724e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9705e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1987e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9591e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8142e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3995e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9351e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5079e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3151e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3640e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2189e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4237e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.5453e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0195
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8700e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.9884e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2217e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5597e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.5202e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0818e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8033e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.4629e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1037e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.1133e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.0266e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.4599e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7614e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1579e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5801e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.3859e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.4077e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3890e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7888e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2123e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6156e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3121e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.3752e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9130e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1513e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1300e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4681e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8004e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4375e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.7148e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.5780e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6372e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8638e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.6480e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7761e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5715e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3418e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0568e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6918e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8688e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7773e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1469e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8251e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0785e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7290e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8621e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7545e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7044e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4175e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 944us/step - loss: 0.0309
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.9107e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4686e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0993e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7066e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3025e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0724e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0627e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.3625e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0046e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1507e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 9.5954e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1977e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.9278e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7133e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0510e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 8.0750e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.9999e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.8717e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 7.6041e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.4850e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.5208e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.3078e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.2973e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.7066e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 6.5518e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.0794e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.6171e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2877e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.3837e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.0887e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.6735e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.9341e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0954e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.1435e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.8274e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.7075e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3289e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.7220e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7920e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9866e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.9681e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.5194e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.9634e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.3550e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.6648e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5569e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7513e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4925e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2165e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0320
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8494e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.3270e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7517e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.0653e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1138e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.3682e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8261e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.0132e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1255e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7500e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3562e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4559e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6212e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0855e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6716e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.6382e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3332e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1601e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0364e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.0270e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.0901e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1653e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.7861e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.7130e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.3248e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2724e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.8266e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3043e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4990e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.6692e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3179e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5689e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5926e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0587e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.2351e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9015e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7620e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0239e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.2635e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5907e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5299e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9526e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7499e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.8101e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2101e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8504e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6151e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8911e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2327e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 950us/step - loss: 0.0260
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.4730e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5686e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1737e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5906e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3923e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4398e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1956e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8237e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6602e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3995e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4178e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1137e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2542e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2401e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.3006e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1827e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.5374e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0371e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.3551e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.7214e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 6.5466e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.8068e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9338e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.9433e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.1000e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.4002e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.2590e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9804e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.5650e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9666e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.4182e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.5322e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3102e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.7435e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0049e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.1045e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7716e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.3645e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.4324e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0159e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.7089e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3683e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.3463e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.6986e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.9261e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.1237e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1125e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.6101e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8871e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0221
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.1434e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.5452e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.0672e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.6590e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1150e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1139e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4417e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6111e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0985e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0926e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0792e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5515e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2572e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0833e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.3198e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.6288e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.1789e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.6146e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.4585e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.9342e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.0214e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.1241e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.4562e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7080e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.6608e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.0591e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.0383e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3578e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.1922e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3792e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3942e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0463e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.8989e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8778e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1864e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7649e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5717e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7721e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.5137e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5898e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4793e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3904e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1831e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9663e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0830e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.4743e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8105e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.8626e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.9572e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0254
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.6388e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9624e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4555e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.2172e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0390e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5060e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1964e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2385e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3147e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.2013e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.2827e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0105e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.9823e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0415e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1462e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.5557e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3645e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.3596e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1245e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.1897e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3705e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3661e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2503e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.6738e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.1502e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7702e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.0907e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.1023e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8062e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7214e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2428e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6078e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.4531e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6848e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7967e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6983e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8169e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4116e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2227e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0770e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8142e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2297e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5871e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4414e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6120e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1551e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2685e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3005e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1534e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0179
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.2604e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7230e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.3855e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.1513e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4861e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7229e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3720e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9349e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4091e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2168e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5031e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4199e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5866e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8907e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7224e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2495e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0062e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2934e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.8105e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0838e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2473e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.8840e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.3651e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.1384e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.0048e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.3963e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9052e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8780e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.9289e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4163e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0259e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.8960e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0407e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.1094e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8796e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5195e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7434e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1196e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6614e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3162e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7257e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6289e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.9743e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2346e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5919e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1986e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4718e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5661e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5304e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0176
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.5877e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.7551e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.8722e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0561e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6822e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9968e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8349e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8722e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3007e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2440e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.3912e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9646e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.2848e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1866e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.6391e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.5822e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.5487e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8150e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0459e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.9631e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4105e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7628e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8336e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5726e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0039e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8443e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6554e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.3505e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.8361e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0766e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3592e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2032e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5733e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8923e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.6387e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.4899e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.8728e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.1660e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7700e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5721e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5644e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6617e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3993e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2719e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6207e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4819e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2948e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8272e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8941e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0147
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5586e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7051e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.7727e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2102e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3988e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4728e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5867e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.3253e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8206e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3870e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3284e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2363e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.6516e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.7425e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9775e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 9.5658e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3420e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3099e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.7394e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2205e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.3259e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5436e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.1189e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5419e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0788e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.3920e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.8831e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.8061e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.3917e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.9668e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.0697e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1094e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.0634e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.9256e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5164e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3729e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9684e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9813e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5722e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9011e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9350e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6524e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7349e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6080e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3463e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5242e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.4398e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2512e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4291e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0154
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.8578e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.9534e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.5962e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2813e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8378e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3569e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7859e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8058e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7998e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3891e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1250e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8931e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5790e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5335e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1882e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2799e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2024e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5129e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0039e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.7785e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.9722e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.8300e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.2240e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1999e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 4.8123e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.0296e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.8559e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9318e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.4096e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.6661e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.6183e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.3429e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.6366e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.5851e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2452e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6892e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3236e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6424e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.1875e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3662e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1924e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7003e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.4562e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3842e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7760e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2142e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7254e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5763e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4402e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0191
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.5185e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1665e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3560e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2489e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7022e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4212e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1615e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5261e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1579e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8036e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5394e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3138e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9972e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6866e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7039e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4623e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4789e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0365e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4583e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1551e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2827e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 9.9325e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.8753e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.5963e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.8829e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.0038e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0041e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.2282e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.8940e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.8347e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.4582e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.4064e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8068e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2352e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.5355e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.9884e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3222e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9798e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9495e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2311e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.1351e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8601e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.7454e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6148e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4243e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3298e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6581e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5679e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4421e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0236
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.6242e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.0260e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5685e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0642e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4772e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4254e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4479e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.6943e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0378e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0284e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.0246e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.1307e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.7421e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9959e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.0308e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8560e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 6.2958e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.3774e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.0408e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.5942e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2187e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4550e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8181e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0724e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8203e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0018e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9791e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6233e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5599e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7226e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4042e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0798e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8251e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7598e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4380e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1332e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3061e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3058e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1274e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1683e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1404e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3919e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2460e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1133e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.0837e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.0590e-06
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.3214e-06
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1993e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.1417e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
149/149 ━━━━━━━━━━━━━━━━━━━━ 1s 987us/step - loss: 0.0250
Epoch 2/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 2.3348e-04
Epoch 3/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.5019e-04
Epoch 4/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 1.3378e-04
Epoch 5/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.5307e-04
Epoch 6/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 1.2857e-04
Epoch 7/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 1.2951e-04
Epoch 8/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 1.2940e-04
Epoch 9/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.2631e-04
Epoch 10/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 1.1909e-04
Epoch 11/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1730e-04
Epoch 12/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.5844e-05
Epoch 13/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.7138e-05
Epoch 14/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 4.2106e-05
Epoch 15/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.4716e-05
Epoch 16/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.1044e-05
Epoch 17/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 4.1963e-05
Epoch 18/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.4284e-05
Epoch 19/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 4.0263e-05
Epoch 20/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.5869e-05
Epoch 21/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 919us/step - loss: 4.0146e-05
Epoch 22/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 3.0149e-05
Epoch 23/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 4.3363e-05
Epoch 24/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.6269e-05
Epoch 25/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.3850e-05
Epoch 26/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.6735e-05
Epoch 27/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 4.0080e-05
Epoch 28/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.9204e-05
Epoch 29/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 946us/step - loss: 3.3347e-05
Epoch 30/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8707e-05
Epoch 31/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6659e-05
Epoch 32/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.8965e-05
Epoch 33/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.0490e-05
Epoch 34/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 3.5497e-05
Epoch 35/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 2.8794e-05
Epoch 36/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 919us/step - loss: 3.5557e-05
Epoch 37/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.5058e-05
Epoch 38/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 3.0534e-05
Epoch 39/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 2.4486e-05
Epoch 40/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 2.6819e-05
Epoch 41/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 2.7070e-05
Epoch 42/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 2.4053e-05
Epoch 43/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 2.7397e-05
Epoch 44/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.0396e-05
Epoch 45/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 2.2865e-05
Epoch 46/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 946us/step - loss: 2.5266e-05
Epoch 47/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 2.9977e-05
Epoch 48/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9783e-05
Epoch 49/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 2.4560e-05
Epoch 50/50
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 3.4074e-05
149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step  
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0186
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.2392e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.1838e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9102e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5986e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.8729e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.2748e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1445e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1349e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.1539e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.3040e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2238e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.1784e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2999e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.9436e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.1832e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7812e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0502e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2563e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.2211e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3565e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2998e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.4670e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2066e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.9530e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8858e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7766e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0898e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8947e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7513e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6903e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5411e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6012e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2230e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4324e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4414e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.4477e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4628e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2637e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2268e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3986e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3184e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1564e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1827e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1851e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2616e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1562e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0647e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1669e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.4552e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step  
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0323
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.4937e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1520e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6571e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3456e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.0701e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.2559e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 7.6897e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 7.3645e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.2874e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8763e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0228e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.3335e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.4115e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.6903e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.5127e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.4451e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.4107e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.0605e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.9414e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.3472e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.9282e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.9108e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 6.3613e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.9634e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.5530e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0053e-04
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6769e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.4798e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 6.4158e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.3058e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.9966e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3064e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.9050e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.7637e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.7250e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1394e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.8818e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3563e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7953e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0125e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7100e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1670e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.5960e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 3.8176e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1487e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.5720e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.6712e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0670e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9940e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0218
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5924e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1742e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4963e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6303e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1261e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.8583e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7157e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.7524e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.8538e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2103e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1236e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.4349e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.5653e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.6763e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 6.7258e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 8.3386e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.6055e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.1714e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9623e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1499e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.4362e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3143e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5421e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 8.0902e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4524e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9803e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.1288e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.6874e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.0215e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.2619e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.8767e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.1258e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7445e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5461e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.1837e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8379e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2106e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.6047e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.3356e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.5194e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4456e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.6096e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0026e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.3332e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.4929e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7334e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.1905e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9666e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6562e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0223
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4132e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0341e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0416e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5199e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2767e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1798e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3270e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2275e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.0467e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.4540e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.4332e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1061e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0047e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3807e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.5202e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 6.0580e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.9254e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.7686e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8274e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1482e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.7424e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9705e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.8902e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.5117e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6571e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2384e-05  
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4183e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0997e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7362e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.9827e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.2626e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0333e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8872e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8587e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.4937e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6346e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7812e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.0970e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4919e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0050e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1399e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0132e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6019e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9295e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0152e-05 
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4770e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6052e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4896e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6235e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0245
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5559e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.5392e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8817e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.1390e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.4444e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.3190e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4441e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8895e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8259e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2936e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2519e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0554e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2839e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 1.6530e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7786e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7008e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8874e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.8337e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8228e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6234e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7549e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0559e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1660e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3469e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.8205e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 2.0365e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7140e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6339e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8293e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9470e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7272e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5980e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8943e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.7876e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6181e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.1525e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2176e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7090e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0268e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7802e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7755e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.5433e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6595e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8025e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9166e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5377e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0479e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.5888e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6021e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0283
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.5470e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2058e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.9512e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0269e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.2754e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.7418e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7432e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 8.2333e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.7640e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1025e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.8916e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7909e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2051e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9221e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2066e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0145e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8266e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6894e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7697e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9914e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1797e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6190e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1180e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8469e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7605e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6633e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8610e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7698e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5431e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3847e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9437e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6560e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5631e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5381e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6346e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3296e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4346e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4498e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4163e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.3082e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5202e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4715e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3249e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5126e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3427e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1229e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.1129e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1500e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4312e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0366
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 4.7285e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7829e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0234e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0619e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1324e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2752e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3857e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0397e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2147e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.7015e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0640e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.1835e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.5180e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.7817e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2804e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.6841e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9060e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.9224e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.9301e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.5438e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7188e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.7300e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.6831e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.4979e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.0964e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.1519e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1211e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9532e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.8757e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.5614e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.0119e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0623e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.2596e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.2518e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8884e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0710e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7545e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2799e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4335e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4944e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8738e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6830e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.6517e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1011e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5522e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9338e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9771e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8066e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9513e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0306
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3354e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7551e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4661e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2595e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2014e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.5665e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1611e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7943e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0651e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7852e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3114e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4280e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0413e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2535e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1278e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0541e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.0425e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1082e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0994e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.9581e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6147e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.1840e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.4341e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.3110e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1397e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.3464e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.9744e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7936e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1897e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9455e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1726e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3182e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9512e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.8036e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0445e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1769e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7888e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6531e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4153e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8189e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.6421e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3326e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2874e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4470e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1530e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7809e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3501e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.2136e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6035e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0211
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.3294e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2503e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0999e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7540e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.9785e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1684e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.7109e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.8557e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.4276e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.0659e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8500e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.9606e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.4922e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 6.0615e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5284e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7857e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.0662e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2240e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1630e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8223e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.8278e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6658e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5675e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.4927e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0790e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2751e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3218e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5453e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6766e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.3745e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7067e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0924e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7639e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4210e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6024e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9151e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8543e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.7688e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1525e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1514e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2794e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2433e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0077e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9652e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9065e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.0549e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1022e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6201e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8881e-05 
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0263
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.4835e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9557e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9893e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3962e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.2087e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4452e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.8127e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4534e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3292e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7329e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0797e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.3829e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0501e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1030e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9689e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.4591e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.8709e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0736e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1088e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.8212e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.4630e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7866e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3791e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9636e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.6721e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3190e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0160e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7824e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3641e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4154e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8182e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8882e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0000e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7753e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8847e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5586e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.1290e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8010e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4815e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4444e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6211e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5394e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6391e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1431e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5270e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7663e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2588e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2258e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5856e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0278
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.2587e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2701e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1370e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6598e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2771e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.0923e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 6.3057e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.1742e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1493e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3389e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.9237e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.5778e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0792e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.3461e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.9818e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1859e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.3443e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.6860e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.9421e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2429e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.8299e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.8233e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2849e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 5.4110e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1941e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9245e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.0785e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2267e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5663e-05  
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4600e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3706e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7185e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6607e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2364e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.0482e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.9319e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5835e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2623e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7013e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4124e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5175e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3689e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.2048e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2742e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7299e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1241e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0618e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1721e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0755e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0225
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.8944e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.9705e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5948e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.9575e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4312e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.0279e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2518e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6971e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1493e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7156e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2396e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7863e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5228e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0283e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.7546e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5059e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6399e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.4653e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1973e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.1216e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 7.8789e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.9624e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.6221e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.9899e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7311e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7612e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6637e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2106e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7265e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9482e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7092e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.8538e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0708e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7398e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9067e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8083e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4011e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2144e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.9026e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.0659e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4441e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0787e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0121e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6422e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6488e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4676e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.4658e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.3054e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.1540e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0280
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 0.0011
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.8632e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.8124e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.3912e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.6353e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1424e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.9037e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.9534e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4477e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4489e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7469e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5126e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5584e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.3402e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.2200e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.3565e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2516e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9577e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.3131e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8422e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 6.9431e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5442e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6848e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.0829e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9114e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.7380e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6928e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9811e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3834e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.2053e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0324e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.8021e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3363e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0514e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5681e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0514e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1870e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.5112e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3052e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.4620e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1750e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8735e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2551e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1798e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.3296e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4916e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.7133e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3247e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.7636e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0227
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8021e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2359e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.0754e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2955e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.3456e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.2994e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.0228e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7215e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.6142e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3244e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9111e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2030e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.7543e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.4732e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.1371e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.8012e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5401e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.7025e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1530e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.3809e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1793e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.5219e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4004e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9973e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.4587e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.8384e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5762e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1328e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5599e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7974e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2546e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5126e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2390e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1027e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1668e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4012e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1219e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2224e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6291e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8339e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.3033e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4320e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1766e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.4477e-06
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0011e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6486e-06
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0008e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.3462e-06
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.8814e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0312
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 0.0013
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 0.0011
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.5858e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.2523e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.6906e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.7402e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4168e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7082e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8359e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0922e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7522e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4212e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3010e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1311e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2656e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0819e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1400e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.9808e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8749e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 8.6748e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.5512e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.2498e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.1410e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2552e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.3256e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.6932e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.3630e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8359e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4788e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.5283e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.4788e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9816e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7942e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0202e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8865e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3790e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1094e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4518e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6890e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.6709e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8070e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6320e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6189e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5813e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3558e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4597e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1464e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3109e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3452e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0226
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.2944e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.9545e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.4838e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.0739e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.9294e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.9317e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.6128e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6237e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1029e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.9771e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0716e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9862e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2034e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4377e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2232e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9197e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9410e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1932e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.1291e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5750e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8182e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0953e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8850e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1303e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0199e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1410e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1636e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0976e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7663e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0522e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3433e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1509e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.0351e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2582e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9256e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7768e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5977e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8855e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7992e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9053e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9256e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5885e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9478e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8479e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8440e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9612e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0930e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9480e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0177e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0382
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7121e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7973e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.2344e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 9.4408e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3373e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.2474e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.3960e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2329e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.2842e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.4705e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4400e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1584e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9682e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0310e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0927e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9250e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4456e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9919e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.9892e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0007e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2131e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2817e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3800e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1695e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1214e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9555e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9909e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.7257e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3216e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0126e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9225e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3476e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8702e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9769e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7716e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0470e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.2101e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0210e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1513e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0853e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2507e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0681e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3045e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9928e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2138e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0797e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9786e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9371e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2291e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0165
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.2848e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6415e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6451e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7926e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2049e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7811e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.5922e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.1369e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5696e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.2582e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3538e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.2518e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.3390e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.2580e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.6178e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.6270e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0138e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.7742e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.6770e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.1892e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.2831e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4921e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.3405e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.9607e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.2243e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.6478e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.4250e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.1100e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.9719e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.3917e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2844e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.6635e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9707e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6196e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9931e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8713e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.7957e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2661e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.7266e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6981e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0589e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4960e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1618e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4360e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3802e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.1058e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7181e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6122e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1283e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0245
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3769e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.9327e-05
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.1735e-05
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0672e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5371e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4924e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3574e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6145e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2050e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4986e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.2363e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3010e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3358e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2030e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9055e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2183e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9090e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9869e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4911e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.1977e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8858e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9006e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.1522e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9518e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2966e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9241e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.0695e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.9346e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9086e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7045e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0347e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1522e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8720e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7239e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2286e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7932e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8516e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8322e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9889e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0839e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5361e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0388e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0822e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0724e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.1531e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7064e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2595e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7754e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1254e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0204
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8889e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.4410e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9184e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2159e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.6026e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7263e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2821e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 7.6513e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.7260e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.8048e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.2635e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.7164e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9532e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6835e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0552e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.4800e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.5919e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.3434e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 8.1168e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.5069e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.8665e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.2201e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.9182e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6600e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7996e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.9774e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0882e-04
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7787e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4520e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2676e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.4123e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4119e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.3432e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.5973e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0453e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.4146e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0936e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.9381e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.0758e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7782e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9633e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6815e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6707e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.1998e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5362e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.3413e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4519e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5550e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2225e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0199
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8507e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0097e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2130e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2280e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.9716e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9301e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8597e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.5747e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2649e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7976e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.1071e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7733e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3185e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0491e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3928e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1216e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2211e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4961e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0276e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.1826e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.9219e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.8748e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.9997e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.3514e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.7355e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4564e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.9511e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3788e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5536e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5316e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.1060e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2400e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8263e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2590e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5542e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9124e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9321e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.3744e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0659e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6065e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6847e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0377e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5408e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6551e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7412e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5493e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5369e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1479e-05  
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9728e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0265
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4727e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9901e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8576e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7728e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4954e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.2975e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0164e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.8122e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.3031e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 9.7079e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.8969e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 5.2402e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.7562e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.4935e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0299e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3165e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9480e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4182e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8887e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.4444e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2510e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8518e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.2283e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.9920e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.6211e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.5658e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6266e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9297e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5921e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.9005e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3273e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1543e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.4966e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.5995e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2676e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1212e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8013e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9643e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.0857e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.0042e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1654e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9419e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.8833e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3124e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9732e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7763e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.8983e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2997e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.1429e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0280
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.4785e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.3635e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.0312e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7354e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0447e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0092e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2880e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7515e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1322e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0106e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0895e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0867e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7600e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.8156e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.3229e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.3124e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1920e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9232e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1588e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.9277e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.0595e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9661e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2194e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0327e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.9332e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.1036e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.3612e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.7383e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.3255e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2547e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6099e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9388e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1305e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.7767e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5769e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5025e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5780e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 2.6609e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.8639e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.0942e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2763e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2966e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3077e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2061e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1022e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3754e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3992e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1191e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7558e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0151 
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8060e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0116e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.8551e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.4590e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.8817e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.9032e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.6553e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.2819e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.4422e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8723e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8669e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9869e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4171e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.1714e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.7623e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9449e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0357e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.2881e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9428e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.5157e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1416e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.8463e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0774e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4523e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.0313e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6683e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5803e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.5123e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.6130e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.4703e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9445e-05 
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7670e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.9883e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.1684e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1355e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0545e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2702e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6247e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6513e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9065e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7172e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4577e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3693e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2564e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0503e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1742e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0608e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.7857e-06
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0396e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
107/107 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0254
Epoch 2/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.8738e-04
Epoch 3/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4266e-04
Epoch 4/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.0363e-04
Epoch 5/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 878us/step - loss: 7.0324e-05
Epoch 6/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4512e-05
Epoch 7/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.7061e-05
Epoch 8/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 962us/step - loss: 4.6700e-05
Epoch 9/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.6722e-05
Epoch 10/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 3.1920e-05
Epoch 11/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 2.1208e-05
Epoch 12/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0560e-05
Epoch 13/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7949e-05
Epoch 14/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2555e-05
Epoch 15/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6880e-05
Epoch 16/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9868e-05
Epoch 17/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6920e-05
Epoch 18/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 887us/step - loss: 1.5564e-05
Epoch 19/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.6601e-05
Epoch 20/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8720e-05
Epoch 21/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8872e-05
Epoch 22/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6582e-05
Epoch 23/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.6029e-05
Epoch 24/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8857e-05 
Epoch 25/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7160e-05
Epoch 26/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6979e-05  
Epoch 27/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3858e-05
Epoch 28/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 887us/step - loss: 1.8375e-05
Epoch 29/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6649e-05 
Epoch 30/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5190e-05
Epoch 31/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 1.5923e-05
Epoch 32/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7097e-05
Epoch 33/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.8414e-05
Epoch 34/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7822e-05
Epoch 35/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.8474e-05
Epoch 36/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1493e-05
Epoch 37/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.7012e-05
Epoch 38/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7069e-05
Epoch 39/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.9703e-05
Epoch 40/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 896us/step - loss: 1.8170e-05
Epoch 41/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 915us/step - loss: 1.4163e-05
Epoch 42/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.6200e-05
Epoch 43/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.5422e-05
Epoch 44/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.9632e-05
Epoch 45/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.8334e-05
Epoch 46/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.6916e-05
Epoch 47/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 2.1329e-05
Epoch 48/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 878us/step - loss: 1.4985e-05
Epoch 49/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 915us/step - loss: 1.8072e-05
Epoch 50/50
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.4175e-05
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0246
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.6005e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 4.5260e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9643e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.5189e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4486e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.5862e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0972e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2574e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4472e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.9863e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5821e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.1173e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.5734e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.9728e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.7945e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.4703e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.4065e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3276e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.4593e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0466e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.7053e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.5154e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.0293e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.3144e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6124e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.9689e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.2615e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.0837e-05  
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.1803e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.5702e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.9297e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3753e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.9200e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.8483e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.1955e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6212e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.3538e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2835e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1141e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3599e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2819e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1606e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.3838e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0133e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4531e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8762e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1306e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9847e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4097e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0347
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.2381e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.1170e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 979us/step - loss: 3.1827e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1718e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9874e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4708e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1340e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.5905e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7563e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2834e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0728e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0228e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0611e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5261e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0550e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.7060e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0367e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0629e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.3823e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.5892e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.4620e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.2957e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3308e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.1969e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.0042e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.8632e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.7129e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8327e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.0144e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.1955e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.3078e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.5237e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7631e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8934e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.5294e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0295e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3080e-05 
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7728e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3440e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9730e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9875e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1961e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8884e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3341e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1911e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7040e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7268e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2235e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9031e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 967us/step - loss: 0.0328
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.8097e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.9861e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.9249e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4182e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6250e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3440e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.8288e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1585e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.6121e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.9258e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.4788e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.0989e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.3436e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1529e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7771e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1396e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1750e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3926e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 7.9920e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.8752e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9409e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 6.7321e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8662e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.8980e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0262e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.7213e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.5434e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.7148e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1227e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4832e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.3132e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.0330e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2975e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8077e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8167e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9397e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4346e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.2596e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9242e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0996e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7246e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5450e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6437e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4185e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9947e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4203e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4010e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5323e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6028e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0274
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7248e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.9171e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0012e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.1514e-05
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1650e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0053e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.8724e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.4412e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8530e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.1667e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.8955e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.0656e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0594e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.6092e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 5.7938e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3898e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 6.0513e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.6496e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.0849e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3076e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6709e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0563e-05 
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6989e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9707e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3537e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0807e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4164e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7090e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2570e-05 
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5105e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4114e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.0212e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0874e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4545e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3769e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3327e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2327e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.0448e-06
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0404e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0105e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0063e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 8.5614e-06
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0441e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0737e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5019e-06
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3041e-06
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.7665e-06
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2775e-05  
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4674e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 995us/step - loss: 0.0303
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 0.0011
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.5484e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9910e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1926e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.0439e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.0104e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.3084e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6299e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5848e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3810e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4862e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0988e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1559e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0487e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0635e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0584e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.7944e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1741e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.5104e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.9655e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0987e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.3391e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.1311e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.3106e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.7621e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.9197e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.6774e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.8638e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.4333e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.0020e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1304e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.8681e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0847e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.2648e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 4.0977e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.3950e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6081e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2339e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.7668e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9685e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3514e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.9763e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4385e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1649e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.9770e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8407e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0633e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.7072e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5401e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0270
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.9334e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.7327e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.5585e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1765e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2403e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0214e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7574e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4900e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6019e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.4837e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 9.3669e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 8.0648e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0616e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0209e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.2999e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.5776e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.7012e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1796e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.7871e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.5625e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.5602e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0694e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3632e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.1713e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6044e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0905e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6640e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.3633e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.5990e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.7382e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5146e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3951e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5202e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9656e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0776e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2928e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4672e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.8744e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9329e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1997e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9611e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7984e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6880e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.9051e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5861e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8442e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4967e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8118e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7710e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step  
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 995us/step - loss: 0.0362
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.2320e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4079e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0355e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8606e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.5583e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3859e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1634e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4501e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2673e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.0770e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4197e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1273e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6298e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.4662e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2859e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1694e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.1621e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3652e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6759e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.1448e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7196e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7733e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.4655e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.0726e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.8767e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7389e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0183e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0299e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4198e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4097e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4165e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4749e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5158e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1226e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1256e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.2878e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1506e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4947e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3102e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4027e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7911e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7597e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3430e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7827e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7091e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5999e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8646e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7871e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1323e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0166
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8697e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2161e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5309e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1882e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.4777e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1348e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2097e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2474e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.7533e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.3561e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.4188e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6297e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.5512e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0132e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.6456e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.9370e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.9406e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.0491e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5878e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.7127e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.2048e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7289e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.4220e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.9979e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.0294e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1018e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.1939e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.6976e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.7699e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.2216e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1718e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.4849e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8393e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1170e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.0662e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7020e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1562e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2168e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.5635e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.8700e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0124e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0450e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2308e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1932e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8668e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7121e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4098e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6775e-05 
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.2392e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0259 
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1125e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.5176e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8987e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0956e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7835e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.6968e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8419e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2441e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6446e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4094e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6698e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.7329e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.9151e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5588e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2092e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.2218e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6104e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2401e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3221e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.2282e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1305e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.0014e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.0942e-04
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.2702e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1259e-04
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.9101e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.2071e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.2228e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.8527e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3760e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.2426e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.7352e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.9702e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.5346e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.6812e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7552e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.8082e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 4.0517e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7660e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0981e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8215e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1399e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3056e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.1437e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6462e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5300e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9097e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1701e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9586e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0270
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.9070e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.9380e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8063e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.6850e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.3526e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5896e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1292e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0391e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0656e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0395e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7023e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.8018e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.8881e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.2984e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.0776e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.8375e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.3675e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.4602e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.0510e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2561e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.2432e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6633e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2243e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4405e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5299e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0039e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.9648e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.6253e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6459e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3462e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5808e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4443e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5683e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3434e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3025e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1718e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.1414e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1529e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0691e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3067e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0833e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1189e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0006e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0111e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0872e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.6835e-06
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.5804e-06
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0970e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.4238e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0214
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.8191e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1651e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.6254e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.9272e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6606e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.2393e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9766e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2111e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5621e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6855e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6013e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6355e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2262e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4739e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7362e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2173e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7745e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4752e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0954e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4051e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1276e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.6925e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3286e-04
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.9273e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.6406e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0811e-04
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.0859e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.7610e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.8651e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.0857e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 7.1194e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.8350e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9808e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.8851e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0749e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5032e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1011e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9976e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.2848e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7288e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4688e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.7187e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0433e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7297e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3215e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3696e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9469e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.1535e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4835e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0255
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.5421e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.1425e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.9222e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8298e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4269e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4877e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8187e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7694e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0364e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2947e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5380e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3582e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2409e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2671e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.9839e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1673e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1626e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4133e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.2581e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6455e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.6778e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.9381e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 8.1025e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.1596e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.1539e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1977e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 5.1804e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.6290e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2869e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7631e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.6236e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2501e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.3802e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4112e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1590e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0657e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8893e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9334e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8528e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7540e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9500e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7311e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7605e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.7491e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.6134e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6501e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6650e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6146e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6036e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0194
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6952e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3048e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1918e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3722e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.3225e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0070e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 9.2440e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0054e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.1649e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.4751e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 8.8328e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6473e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.0190e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.9946e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.3701e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1002e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.1777e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.9287e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.4581e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.3769e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.2990e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7889e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8151e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.0520e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.7168e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.1239e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6638e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6132e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.2124e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.5073e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2113e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.6111e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9188e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6733e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5992e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2052e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.2955e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6228e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8851e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5896e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8913e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5851e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0074e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6449e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5955e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6681e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2121e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2812e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2661e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0226
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.1546e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8891e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3411e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4610e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.1408e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7156e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4269e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4200e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5343e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0787e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0135e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5529e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.0979e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4237e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.6201e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.5187e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 6.7524e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2446e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2502e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.0033e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.6600e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.6506e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.0960e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.7777e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 7.8618e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7526e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3783e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.9341e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7341e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9743e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2970e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7204e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.0029e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.2607e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4020e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3473e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.8581e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9017e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2952e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4660e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7725e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8376e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7126e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4942e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6534e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5359e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8109e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6919e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5800e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0242
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.2047e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.0806e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.7829e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2867e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5043e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.0222e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.0804e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2327e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.7074e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3604e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9699e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5929e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3004e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5783e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1195e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0930e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0706e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1740e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.0516e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 8.9775e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.1092e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.9505e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1626e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.1421e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0828e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.3040e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2393e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.3741e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9025e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8383e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.8857e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6577e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.0219e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6566e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8566e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7824e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6196e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6665e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1446e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.7786e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.1564e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5695e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0461e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5490e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6057e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2976e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5587e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4444e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.0391e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step  
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 978us/step - loss: 0.0240
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6151e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.2104e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1916e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1867e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4505e-04 
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.4331e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.9954e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2331e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.6867e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1412e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.2125e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.3696e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0308e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0034e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.6111e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.2226e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.0900e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.1367e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9839e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0198e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.1152e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.3372e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.2904e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.2145e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.5984e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.8438e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.6226e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.1734e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.1781e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.0834e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.1145e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9466e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5461e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.5345e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3156e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.8070e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2459e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1893e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4946e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4786e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5918e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9494e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6252e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2833e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4728e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4140e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3532e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5111e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1930e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0318
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.3081e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7802e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.8883e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4626e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7235e-04  
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9442e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1716e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7714e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.2763e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 9.5934e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1059e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.9282e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 6.8511e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 6.2542e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.5535e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.8377e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8449e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.5715e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2779e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3160e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4619e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.5495e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9873e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.6329e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8669e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1101e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6616e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2321e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0489e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6236e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8580e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9215e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9611e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0485e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5853e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9592e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4241e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0658e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.9177e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.8335e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3140e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3399e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6581e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6165e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7551e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6112e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9962e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7609e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7882e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0236
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9127e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6525e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8166e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2766e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.2784e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5587e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0616e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4389e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.6858e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6484e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1012e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.2481e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0444e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7634e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.3412e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.3484e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.0814e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.7195e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7133e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4665e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8579e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.0737e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.3677e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8836e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4224e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.1343e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1429e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4005e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0079e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2097e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9813e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6621e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0154e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9483e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6339e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9086e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5607e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0752e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3320e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4797e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6033e-05 
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3276e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3020e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.3091e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.1439e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0101e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.1493e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.4332e-06
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.1880e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0280
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7029e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4573e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4352e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0045e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0007e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.9336e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.0730e-05
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.2802e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0490e-05
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0020e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9687e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1049e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.6620e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.0918e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4419e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.0245e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.6521e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.9558e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4782e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5460e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5170e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2175e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.8608e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7232e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6137e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.0016e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1214e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.6319e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.5854e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1562e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.5736e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.2124e-05 
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 3.6449e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.0534e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3249e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4669e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8210e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.4598e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0663e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.4696e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5709e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7005e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2393e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1322e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7852e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7019e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7411e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3004e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5999e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0233
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.7536e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8682e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.6241e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.1470e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.3790e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.4981e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2753e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4852e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6441e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9168e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5755e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2313e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1984e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2779e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7305e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1671e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.7966e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4720e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6271e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5152e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6316e-04
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8407e-04
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4059e-04
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2519e-04
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.2211e-04
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2159e-04 
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1261e-04
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.5329e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.4924e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.3020e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.2853e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7360e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.1519e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.0238e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.9037e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.5484e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.2220e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.6598e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.9047e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0703e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2269e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1204e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1331e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.8281e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5947e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8286e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.2218e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2734e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3642e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0217
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.6910e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.4071e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0466e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.0256e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2709e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9558e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4562e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0349e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4726e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2143e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0743e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1009e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9190e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.1292e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 8.4251e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.7312e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0724e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9671e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.7870e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.0953e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.3670e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5036e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 9.9589e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 7.0912e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.9038e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.1080e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.0685e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.4004e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.8124e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.4375e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8565e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6049e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 6.9497e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.8886e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2326e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8529e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3603e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2643e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8083e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9491e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4369e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7461e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8073e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.2332e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2164e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6708e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3671e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3107e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9040e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0304
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.0501e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4626e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3896e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1896e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0337e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.8916e-05
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3475e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.5296e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2607e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.2830e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1344e-04 
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.2104e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0600e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.5860e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.4240e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0745e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0974e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.8138e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9569e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 9.6084e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.3079e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.3577e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3170e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.9904e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.7762e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.9506e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.2828e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3428e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.9548e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9600e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.8679e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1074e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2422e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0725e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.1523e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6722e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5699e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2748e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7806e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3782e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.0210e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.8553e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9057e-05  
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7690e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7055e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3326e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6059e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3394e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3521e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0238
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9144e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6113e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.9976e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9791e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4192e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4719e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7643e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.6835e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.6397e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5648e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7137e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9477e-04
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3624e-04
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7284e-04
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7882e-04
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1916e-04
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2845e-04
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.0944e-04
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1954e-04
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1924e-04
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.1275e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0732e-04
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4160e-04
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0491e-04
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0968e-04
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.8210e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.4524e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9503e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.1994e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7299e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6759e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.5018e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.3081e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.0730e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8203e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9415e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.4312e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6140e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5987e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.9523e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 3.0246e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2383e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2697e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7798e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.5769e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3934e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1086e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3678e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1210e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 950us/step - loss: 0.0284
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.6166e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.6977e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0400e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7663e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0734e-04
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9431e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9676e-04 
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6445e-04
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3912e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1139e-04
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0957e-04
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.0413e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.4546e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2428e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.9346e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.4477e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.8184e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.2043e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8156e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.4272e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0058e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.3927e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2456e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.8948e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.1329e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.4735e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4948e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3372e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9323e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1927e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.2231e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2872e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3882e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6526e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1675e-05
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 3.3668e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2573e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1784e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4249e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7835e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5556e-05
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2790e-05
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1547e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5515e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9167e-05
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8769e-05
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0320e-05
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7700e-05
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9538e-05
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
No description has been provided for this image
No description has been provided for this image
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.
  df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0245
Epoch 2/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.0477e-04
Epoch 3/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9595e-04
Epoch 4/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.5860e-04
Epoch 5/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4406e-04
Epoch 6/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.0335e-05
Epoch 7/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4537e-04
Epoch 8/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6644e-04
Epoch 9/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 9.9606e-05
Epoch 10/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1602e-04
Epoch 11/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 6.8369e-05
Epoch 12/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.6520e-05
Epoch 13/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.5333e-05
Epoch 14/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5612e-05
Epoch 15/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.5655e-05
Epoch 16/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.5708e-05
Epoch 17/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7747e-05
Epoch 18/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6945e-05
Epoch 19/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7663e-05
Epoch 20/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8075e-05
Epoch 21/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5487e-05
Epoch 22/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.8553e-05
Epoch 23/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5861e-05
Epoch 24/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.5228e-05
Epoch 25/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6080e-05
Epoch 26/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3325e-05
Epoch 27/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7166e-05
Epoch 28/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2500e-05
Epoch 29/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1312e-05
Epoch 30/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1513e-05
Epoch 31/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0280e-05
Epoch 32/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3059e-05
Epoch 33/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2035e-05
Epoch 34/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1734e-05
Epoch 35/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1382e-05
Epoch 36/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 9.7618e-06
Epoch 37/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0593e-05
Epoch 38/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1497e-05
Epoch 39/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0027e-05
Epoch 40/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0446e-05
Epoch 41/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2686e-05
Epoch 42/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.8921e-06
Epoch 43/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.6025e-06
Epoch 44/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0150e-05
Epoch 45/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2443e-05
Epoch 46/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.8960e-06
Epoch 47/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.1038e-06
Epoch 48/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.8819e-06
Epoch 49/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.3785e-06
Epoch 50/50
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.6949e-06
179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
No description has been provided for this image
No description has been provided for this image